Franchisee economics

Franchisee model: site-level cash flow, payback and stress-tested unit economics.

The franchisee plan is where the site-level economics become concrete: incremental capital, net AI revenue after royalty, utility costs, payback, scenario analysis and downside resilience.

$3.4MApproximate total capital investment per site in the main franchisee plan.

35 GPUsReference midpoint deployment used in several site-level calculations.

$1.09MApproximate Year-1 cumulative incremental pre-tax income in the payback example.

~4.5 yearsApproximate payback with first major hardware refresh included.

Franchisee cash flow page — **Cash flow**Long-range accumulated franchisee cash flow.

Franchisee incremental profit page — **Incremental profit**Site-level incremental profit and ROI scenario.

Franchisee payback period page — **Payback period**Payback on incremental capital.

Leased-site scenario analysis page — **Scenario analysis — leased site**Leased-site economics comparison.

Owned-site scenario analysis page — **Scenario analysis — owned site**Owned-site economics comparison.

Franchisee site-value page — **Site value estimate**Potential mature-location value range.

Franchisee stress-test conclusion — **Stress-test conclusion**Downside-resilience stress test.

Physical-site concept

Compute becomes another utility stream inside an operating business.

The physical model is built around real recurring thermal loads rather than hypothetical district-heat demand. Car washes and laundries can absorb useful heat, while the distributed network supplies compute revenue and flexible workload placement. At network scale, dedicated dark fibre and NVIDIA Spectrum-XGS are intended to make those independently operated sites function as a unified compute fabric rather than isolated edge nodes.

Open science downloads

Current papers, methods and model limits.

First page of the current heat-sink-constrained distributed AI infrastructure manuscript

Heat-sink-constrained siting of distributed AI infrastructure: energy, carbon and water impacts in Los Angeles

The current deterministic hourly study places 6,160 B300-equivalent accelerators across 120 representative car-wash and laundromat hosts and tests useful heat, carbon, water, climate and metropolitan-network constraints. Its network programme uses NVIDIA Spectrum-XGS as the scale-across layer over dedicated optical fibre and specifies fibre-spool and multi-site experiments to test whether distributed sites can match co-located clusters — the key experiment behind the project’s objective of proving that mega data centres can become obsolete as the default architecture for many workloads.

Current paper PDF Model + files

First page of the current mechanism-resolved fanless photovoltaic-thermal heat-rejection manuscript

Mechanism-resolved modeling and adaptive control of a fanless photovoltaic-thermal heat-rejection array

The current 170 m² roof-array study resolves passive and wetted mechanisms separately, quantifies the water-capacity trade-off, rejects array-wide ionic wind on energetic grounds and tests roof-area matching from micro facilities to hyperscale.

Current paper PDF DOCX Model + files

The goal: prove the mega data centre is no longer necessary.

Cloud Wash Laundry is being designed around a direct technical challenge to the conventional mega-campus model: place compute where useful heat already exists, then connect those heat-sink-sited AI factories with dedicated dark fibre and NVIDIA Spectrum-XGS scale-across networking so the distributed sites can operate as one metropolitan compute fabric.

The dark fibre is the dedicated optical transport; Spectrum-XGS is NVIDIA’s scale-across networking layer. The project’s proposed fibre-spool testbed and multi-site field pilot are intended to determine whether distributed sites can match co-located clusters on workload-level measures such as time-to-result and energy per token.

Measured next. Modeled now. Heat-sink constrained.

Current design-stage results combine the 120-host Los Angeles heat-sink-siting model with the updated fanless heat-rejection screening figure. They are screening outputs, not field-certified performance. In parallel, the network programme tests whether NVIDIA Spectrum-XGS scale-across networking over dedicated dark fibre can make geographically separated AI factories behave as one compute system. The mega-data-centre conclusion remains an experimental objective, not an already validated result.